Abstract：
UNASSIGNED: : Erythroid cells play important roles in hemostasis and disease. However, there is still significant knowledge gap regarding stress erythropoiesis.
UNASSIGNED: : Two single-cell RNAseq datasets of erythroid cells on GEO with accession numbers GSE149938 and GSE184916 were obtained. The datasets from two sources, bone marrow and peripheral blood were analyzed using Seurat v4.1.1, and other tools in R. QC metrics were performed, data were normalized and scaled. Principal components that capture the variation of the data were determined. In clustering the cells, KNN graph was constructed and Louvain algorithm was applied to optimize the standard modularity function. Clusters were defined via differential expression of features.
UNASSIGNED: We identified 9 different cell types, with a particular cluster representing the stress erythroids. The clusters showed differentially expressed genes as observed from the gene signature plot. The stress erythroid cluster differentially expressed some genes including ALAS2, HEMGN, and GUK1.
UNASSIGNED: The erythroid population was found to be heterogeneous, with a distinct sub-cell type constituting the stress erythroids; this may have important implications for our knowledge of steady-state and stress erythropoiesis, and the markers found in this cluster may prove useful for future research into the dynamics of stress erythroid progenitor cell differentiation.
UNASSIGNED: : Two single-cell RNAseq datasets of erythroid cells on GEO with accession numbers GSE149938 and GSE184916 were obtained. The datasets from two sources, bone marrow and peripheral blood were analyzed using Seurat v4.1.1, and other tools in R. QC metrics were performed, data were normalized and scaled. Principal components that capture the variation of the data were determined. In clustering the cells, KNN graph was constructed and Louvain algorithm was applied to optimize the standard modularity function. Clusters were defined via differential expression of features.
UNASSIGNED: We identified 9 different cell types, with a particular cluster representing the stress erythroids. The clusters showed differentially expressed genes as observed from the gene signature plot. The stress erythroid cluster differentially expressed some genes including ALAS2, HEMGN, and GUK1.
UNASSIGNED: The erythroid population was found to be heterogeneous, with a distinct sub-cell type constituting the stress erythroids; this may have important implications for our knowledge of steady-state and stress erythropoiesis, and the markers found in this cluster may prove useful for future research into the dynamics of stress erythroid progenitor cell differentiation.
摘要：
■：红系细胞在止血和疾病中起重要作用。然而,关于应激性红细胞生成仍然存在显著的知识差距。
■：获得了GEO上的红系细胞的两个单细胞RNAseq数据集，登录号为GSE149938和GSE184916。来自两个来源的数据集，使用Seuratv4.1.1和R中的其他工具分析骨髓和外周血。数据进行归一化和缩放。确定了捕获数据变化的主成分。在对细胞进行聚类时，构造了KNN图，并应用Louvain算法优化了标准模块化函数。通过特征的差异表达来定义簇。
■我们确定了9种不同的细胞类型，一个代表应激红细胞的特定簇。如从基因签名图观察到的,簇显示差异表达的基因。应激红系簇差异表达一些基因，包括ALAS2，HEMGN，GUK1
■红系群体被发现是异质的，具有不同的亚细胞类型构成应激红细胞；这可能对我们的稳态和应激红细胞生成知识具有重要意义，并且在该簇中发现的标记可能被证明对将来研究应激红系祖细胞分化的动力学有用。
■：获得了GEO上的红系细胞的两个单细胞RNAseq数据集，登录号为GSE149938和GSE184916。来自两个来源的数据集，使用Seuratv4.1.1和R中的其他工具分析骨髓和外周血。数据进行归一化和缩放。确定了捕获数据变化的主成分。在对细胞进行聚类时，构造了KNN图，并应用Louvain算法优化了标准模块化函数。通过特征的差异表达来定义簇。
■我们确定了9种不同的细胞类型，一个代表应激红细胞的特定簇。如从基因签名图观察到的,簇显示差异表达的基因。应激红系簇差异表达一些基因，包括ALAS2，HEMGN，GUK1
■红系群体被发现是异质的，具有不同的亚细胞类型构成应激红细胞；这可能对我们的稳态和应激红细胞生成知识具有重要意义，并且在该簇中发现的标记可能被证明对将来研究应激红系祖细胞分化的动力学有用。
